Scene segmentation without labeling by combining images and LiDAR with Drive&Segment
Scene segmentation without labeling by combining images and LiDAR with Drive&Segment
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
arXiv paper abstract https://arxiv.org/abs/2203.11160
arXiv PDF paper https://arxiv.org/pdf/2203.11160.pdf
Project page https://vobecant.github.io/DriveAndSegment/
... investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city.
... First, ... propose ... cross-modal unsupervised learning of semantic image segmentation by leveraging synchronized LiDAR and image data.
... method is the use of an object proposal module that analyzes the LiDAR point cloud to obtain proposals for spatially consistent objects.
Second, ... show that these 3D object proposals can be aligned with the input images and reliably clustered into semantically meaningful pseudo-classes.
Finally, ... develop a cross-modal distillation approach that leverages image data partially annotated with the resulting pseudo-classes to train a transformer-based model for image semantic segmentation.
... without any finetuning, and demonstrate significant improvements compared to the current state of the art ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Comments